arXiv:2601.12186v3 Announce Type: replace-cross Abstract: Multi-domain thinking verifiers trained via Reinforcement Learning with Verifiable Rewards (RLVR) are a cornerstone of modern post-training. However, their adoption in code generation has lagged behind that of execution feedback due to the prohibitive costs of the full RLVR pipeline. In this work, we ablate three primary choices along the performance-cost trade-off in RLVR: intermediate thinking traces, learning from negative samples, and on-policy training. We introduce Aletheia, a controlled, execution-grounded testbed to facilitate a
The rapid advancement in AI for code generation necessitates more efficient and cost-effective verification methods, making improvements to RLVR pipelines critically timely.
Improving the efficiency of RLVR for code verifiers can significantly accelerate the development of more reliable and autonomous AI code generation tools, impacting software development cycles and trust.
The research by Aletheia helps to reduce the prohibitive costs associated with full RLVR pipelines, making advanced verification techniques more accessible for AI-driven code generation.
- · AI code generation platforms
- · Software developers
- · AI research institutions
- · Reinforcement learning practitioners
- · Manual code verification services
- · Less efficient AI development methodologies
More robust and reliable AI-generated code becomes achievable at scale.
Increased adoption of AI in critical software infrastructure as verification costs decrease.
The role of human programmers shifts further towards oversight and high-level design, rather than granular coding and debugging.
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Read at arXiv cs.AI